Physical therapy (PT) is a key component of many rehabilitation regimens, such as treatments for Parkinson’s disease (PD). However, there are shortages of physical therapists and adherence to self-guided PT is low. Robots have the potential to support physical therapists and increase adherence to self-guided PT, but prior robotic systems have been large and immobile, which can be a barrier to use in homes and clinics. We present Stretch with Stretch (SWS), a novel robotic system for leading stretching exercise games for older adults with PD. SWS consists of a compact and lightweight mobile manipulator (Hello Robot Stretch RE1) that visually and verbally guides users through PT exercises. The robot’s soft end effector serves as a target that users repetitively reach towards and press with a hand, foot, or knee. For each exercise, target locations are customized for the individual via a visually estimated kinematic model, a haptically estimated range of motion, and the person’s exercise performance. The system includes sound effects and verbal feedback from the robot to keep users engaged throughout a session and augment physical exercise with cognitive exercise. We conducted a user study for which people with PD (n = 10) performed 6 exercises with the system. Participants perceived the SWS to be useful and easy to use. They also reported mild to moderate perceived exertion (RPE).

We are developing a natural language interface for human robot interaction that implements reasoning about deep semantics in natural language. To realize the required deep analysis, we employ methods from cognitive linguistics, namely the modular and compositional framework of Embodied Construction Grammar (ECG) [18]. Using ECG, robots are able to solve fine-grained reference resolution problems and other issues related to deep semantics and compositionality of natural language. This also includes verbal interaction with humans to clarify commands and queries that are too ambiguous to be executed safely. We implement our NLU framework as a ROS package and present proof-of-concept scenarios with different robots, as well as a survey on the state of the art in knowledge-based language HRI.

Communities of Learning Practice is an innovative paradigm focused on providing appropriate technological support to both formal and especially informal learning groups who are chiefly formed by non-technical people and who lack of the necessary resources to acquire such systems. Typically, students who are often separated by geography and/or time have the need to meet each other after classes in small study groups to carry out specific learning activities assigned during the formal learning process. However, the lack of suitable and available groupware applications makes it difficult for these groups of learners to collaborate and achieve their specific learning goals. In addition, the lack of democratic decision-making mechanisms is a main handicap to substitute the central authority of knowledge presented in formal learning. From the literature, the provision of specific support to informal collaborative learning has, to the best of our knowledge, been little investigated. To fill this gap, we present an ongoing work that will result in a democratic web-based groupware learning system especially designed to provide support for informal collaborative learning over the Internet. Moreover, an important purpose of this software is to provide advanced mechanisms of information management from the group activity for its further use in extracting and providing effective knowledge on interaction behavior. Indeed, this issue represents a fundamental requirement for current collaborative learning environments in order to adequately regulate the learning process as well as to enhance learning group’s participation by means of providing appropriate awareness and feedback. In this paper, we describe the main guidelines that conducted the requirements and design of this application as well as introduce the underlying groupware platform, called CoPE, that provides the essential functional support for democratic groupware.

Computer supported cooperative work (CSCW), and e-learning have overlapping, but not identical design and use profiles. We suggest that democratically organized CSCW systems provide an ideal platform for organizing and encouraging group interactions in e-learning situations where physical meetings are not feasible. Few attempts at democratic CSCW systems have been made, we argue, largely because the necessary development resources are concentrated in large hierarchical organizations which have little apparent use for such systems. The CoPE CSCW system does seem to provide a promising platform for extension to e-learning environment, and there is an ongoing effort to do this.

To perform automatic, unconscious inference, the human brain must solve the binding problem by correctly grouping properties with objects. Temporal binding models like SHRUTI already suggest much of how this might be done in a connectionist and localist way by using temporal synchrony. We propose a set of alternatives to temporal synchrony mechanisms that instead use short signatures. This serves two functions: it allows us to explore an additional biologically plausible alternative, and it allows us to extend and improve the capabilities of these models. These extensions model the human ability to both perform unification and handle multiple instantiations of logical terms. To verify our model's feasibility, we simulate it with a computer system modeling simple, neuron-like computations.

This paper describes an effort at UC Berkeley and the International Computer Science Institute to develop a supercomputer for artificial neural network applications. Our perspective has been strongly influenced by earlier experiences with the construction and use of a simpler machine. In particular, we have observed Amdahl's Law in action in our designs and those of others. These observations inspire attention to many factors beyond fast multiply-accumulate arithmetic. We describe a number of these factors along with rough expressions for their influence and then give the applications targets, machine goals and the system architecture for the machine we are currently designing.

This paper describes programming constructs and system support functions that are intended to facilitate the programming of reliable distributed systems. The systems considered include very different kinds of computers communicating through a network. Such a heterogeneous network offers a number of advantages to designers of applications software. Different machines emphasize different capabilities and many problems naturally break down into subproblems that are best solved with specialized resources. There is clearly a need for programming tools that will allow users to exploit this kind of environment.

Distributed computing with widely separated machines is a subject of growing theoretical and practical interest. This paper attempts to present a framework for the analysis of message-based distributed computations. This is done in the context of the classical critical section problem and the high-level language, PLITS. The proof techniques described are based on the use of finite-state machines which characterize the external behavior of each module in the distributed computation.

Rochester's Intelligent Gateway (RIG) system provides convenient access to a wide range of computing facilities. The system includes five large minicomputers in a very fast internal network, disk and tape storage, a printer/plotter and a number of display terminals. These are connected to larger campus machines (IBM 360/65 and DEC KL10) and to the ARPANET. The operating system and other software support for such a system present some interesting design problems. This paper contains a high-level technical discussion of the software designs, many of which will be treated in more detail in subsequent reports.

The Nerode realization technique for synthesizing finite-state machines from their associated right-invariant equivalence relations is modified to give a method for synthesizing machines from finite subsets of their input-output behavior. The synthesis procedure includes a parameter that one may adjust to obtain machines that represent the desired behavior with varying degrees of accuracy and that consequently have varying complexities. We discuss some of the uses of the method, including an application to a sequential learning problem.